Epilepsy is a neurological disorder characterized by abnormal electrical activity of the brain that causes recurrent seizures. Electroencephalography (EEG) data can help in the detection of such seizures. However, labelled EEG datasets are scarce because the labelling process of this type of data is a time-consuming and expertise-requiring activity. On the other hand, vast amounts of unlabelled data are available. The objective of this work is to understand if and how it is possible to exploit unannotated datasets for seizure detection on EEG data. Since supervised methods are limited by the amount of labelled data available, the thesis focuses on unsupervised and self-supervised methods.